Deep Generative Modeling
This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models,...
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| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
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Cham :
Springer International Publishing : Imprint: Springer,
2024.
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| Vydání: | 2nd ed. 2024. |
| Témata: | |
| On-line přístup: | https://doi.org/10.1007/978-3-031-64087-2 |
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| 100 | 1 | |a Tomczak, Jakub M. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Deep Generative Modeling |h [electronic resource] / |c by Jakub M. Tomczak. |
| 250 | |a 2nd ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XXIII, 313 p. 179 illus.). |b 170 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of colour is Not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background colour |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All Non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a Chapter 1 Why Deep Generative Modeling? -- Chapter 2 Probabilistic modeling: From Mixture Models to Probabilistic Circuits -- Chapter 3 Autoregressive Models -- Chapter 4 Flow-based Models -- Chapter 5 Latent Variable Models -- Chapter 6 Hybrid Modeling -- Chapter 7 Energy-based Models -- Chapter 8 Generative Adversarial Networks -- Chapter 9 Score-based Generative Models -- Chapter 10 Deep Generative Modeling for Neural Compression -- Chapter 11 From Large Language Models to Generative AI. | |
| 520 | |a This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based Models, and Large Language Models. In addition, Generative AI Systems are discussed, demonstrating how deep generative models can be used for neural compression. All chapters are accompanied by code snippets that help to better understand the modeling frameworks presented. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics of machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It should appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics who wish to get familiar with deep generative modeling. In order to engage with a reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on the author's GitHub site: github.com/jmtomczak/intro_dgm The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them. | ||
| 532 | 8 | |a Accessibility summary: This PDF does Not fully comply with PDF/UA standards but does include limited screen reader support, descriptions for Non-text content (such as images and graphs), bookmarks for easy navigation, and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognise the importance of accessibility and welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us via https://www.springernature.com/gp/info/accessibility | |
| 532 | 8 | |a Inaccessible, or known limited accessibility | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computer science |x Mathematics. | |
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Probability and Statistics in Computer Science. |
| 650 | 2 | 4 | |a Computer Modelling. |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031640865 |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031640896 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-64087-2 |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||